Knowledge Discovery and Causality in Urban City Traffic
Damien Fay, Gautam Malviya Thakur, Pan Hui, Ahmed Helmy · 2013
The increase in number of vehicles has created problems in many cities across the globe. Building comprehensive knowledge base about global city dynamics and traffic distribution is a key step to provide fundamental solution to the problems. In this paper, we examine a readily available data source; the existing infrastructure of traffic cameras around the world. We have collected real time traffic data from 2,700 public online traffic camera distributed across 10 cities in four continents for a duration of six months. Our platform allows us to automatically search public cameras, collect and process imagery data, remove outliers, and extract traffic density from those images in a highly scalable way. A time series model employing a co-integrated vector autoregression model is presented in which traffic forecasts may be produced and regions of the city not well observed may be suggested. In addition, a topological comparison of six of these networks is presented.